---
title: "RAVE Pipeline Markdown Template"
output:
  html_document: default
  pdf_document: default
---

```{r setup, include = FALSE}
# This code block sets up the engine environment
# Please do not remove me
raveio::pipeline_setup_rmd("TEMPLATE")
```

## Introduction

A RAVE pipeline markdown is an interactive notebook that can keep your notes, code blocks, and corresponding results together, generating reports in various formats such as `PDF`, `html`, `Word`, `PowerPoint`. 

The note parts are simply `markdown`s - the same as `jupyter notebook`, or github documentations. The code blocks support `R`, `python`, `c++`. When you hit the `Knit` button in this code editor panel, the r-markdown file will be compiled, generating reproducible documentation.

With carefully designed structures, this r-markdown file will automatically generate `RAVE` pipeline scripts during the compilation. The pipeline script can be used by `RAVE` to convert your pipeline into interactive dashboard application. (This feature is currently under development)

## "RAVE" Pipeline Code Block

A `RAVE` pipeline markdown code block starts with ` ```{rave ... `. The block label following `rave` informative description of the target. After the target, the following RAVE-specific parameters configures how the block should be treated:

* `language`: specifies the programming language used; choices are: `R`, `python`
* `export`: variable name to be exported that will be available to the rest chunks
* `depends`: indicates current block depends on variables generated from other blocks; this helps `RAVE` to build non-interactive pipeline scripts internally. For blocks written in `R`, the dependence can be automatically determined.

Other parameters are available at [this `rmarkdown` book](https://bookdown.org/yihui/rmarkdown/)

## An Example

In the rest of the documentation, let's import the subject power data, baseline, and plot the collapsed mean as image.

#### Step 1: Create `RAVE` subject's instances

Noting that all the items in the `settings.yaml` are available as variables.

```{rave load_subject, language = "R", export = "subject"}
library(raveio)
subject <- RAVESubject$new(
  project_name = project_name, 
  subject_code = subject_code
)
print(subject)

# List subject details
subject$epoch_names
subject$reference_names
subject$blocks
subject$electrodes

# Check if notch & wavelet has been applied on all electrodes
all(subject$notch_filtered)
all(subject$has_wavelet)
```

With `export="subject"`, the subject variable will be registered for the following chunks to use. Be aware that all other variables created in this block will not be exposed.

#### Step 2: Load epoch and reference information

```{rave load_epoch, language = "R", export = "epoch_instance"}
epoch_instance <- raveio::RAVEEpoch$new(subject, epoch)
head(epoch_instance$update_table())
```

```{rave load_reference, language = "R", export = "reference_table"}
reference_table <- subject$meta_data(
  meta_type = "reference", reference)
head(reference_table)
```

#### Step 3: Initialize and load power data

Initialize the electrode instances and register the epoch, reference information

```{rave load_electrodes, language = "R", export = "electrode_instances"}
requested_electrodes <- dipsaus::parse_svec(electrodes)
requested_electrodes <- requested_electrodes[
  requested_electrodes %in% subject$electrodes]
electrode_instances <- lapply(requested_electrodes, function(ei){
  e <- raveio::new_electrode(subject, number = ei, signal_type = "LFP")
  
  # set epoch
  e$set_epoch(epoch_instance)

  # set epoch range (-1 to 2 seconds relative to onset)
  e$trial_intervals <- intervals

  # set reference
  ref_name <- subset(reference_table, Electrode == ei)[["Reference"]]
  e$set_reference(ref_name)
  e
})
names(electrode_instances) <- requested_electrodes
```


Start to load power. Here also create cache to the `RAVE` cache directory.

```{rave load_and_cache_power, language = "R", export = "power"}
power_list <- lapply(electrode_instances, function(e){
  e$load_data("power")
})
target <- normalizePath(file.path("shared", "cache", "power"),
                        mustWork = FALSE)
  
if(dir.exists(target)){
  unlink(target, recursive = TRUE, force = TRUE)
} else {
  raveio::dir_create2(dirname(target))
}
power <- filearray::filearray_bind(
  .list = power_list, symlink = TRUE, 
  filebase = target)

if(length(power_list)){
  dnames <- dimnames(power_list[[1]])
  dnames[[4]] <- as.integer(names(electrode_instances))
  dimnames(power) <- dnames
} else {
  stop("No power data not loaded")
}
```

#### Step 4: Baseline correction

```{rave calculate_baseline, language = "R", export = "baseline_data"}
dim <- dim(power)
dim[length(dim)] <- 1
plen <- prod(dim)
target <- file.path(electrode_instances[[1]]$cache_root, "..")
raveio::dir_create2(target)
target <- normalizePath(target, mustWork = TRUE)
target <- file.path(target, "_baseline")
if(dir.exists(target)){
  output <- filearray::filearray_load(target, "readwrite")
  if(!all(dim(output) == dim(power))){
    output$delete()
  }
  if(!identical(dimnames(output), dimnames(power))){
    dimnames(output) <- dimnames(power)
  }
}
if(!dir.exists(target)){
  output <- filearray::filearray_create(
    filebase = target,
    dimension = dim(power),
    type = "float",
    partition_size = 1L
  )
}

bl_range <- unlist(baseline)
dnames <- dimnames(power)
baseline_indexpoints <- which(
  dnames$Time >= bl_range[[1]] & dnames$Time <= bl_range[[2]])

filearray::fmap(power, function(input){
  x <- input[[1]]
  dim(x) <- dim
  bl <- dipsaus::baseline_array(
    x, along_dim = 2L,
    baseline_indexpoints = baseline_indexpoints,
    method = baseline_method
  )
  bl
}, .y = output, .input_size = plen)

baseline_data <- output
```


#### Step 5: Collapse baseline data

```{rave collapse_by_trial_and_electrodes, language = "R", export = "collapsed"}

dim <- dim(baseline_data)
dim[length(dim)] <- 1
plen <- prod(dim)
collase_by_trial <- filearray::fmap2(baseline_data, function(input){
  x <- input[[1]]
  dim(x) <- dim
  dipsaus::collapse(x, keep = c(2,1), average = TRUE)
}, .input_size = plen, .simplify = TRUE)

collapsed <- list(
  collase_by_trial = collase_by_trial,
  collase_by_trial_electrodes = dipsaus::collapse(
    collase_by_trial, keep = c(1, 2), average = TRUE)
)
```

```{r}
brain <- threeBrain::freesurfer_brain2(
  fs_subject_folder = subject$freesurfer_path, subject_name = subject$subject_code
)
brain$plot()
```

#### Step 6: Visualize

```{rave plot_heatmap_collase_by_trial_electrodes, export = "collase_by_trial_timestamp", cue="always"}

# Timestamp of when the plot is created
collase_by_trial_timestamp <- Sys.time()

# visualize
dnames <- power$dimnames()

image(
  collapsed$collase_by_trial_electrodes,
  y = dnames$Frequency,
  ylab = "Frequency (Hz)",
  x = dnames$Time, 
  xlab = "Time (s)",
  main = "Mean power across trial and electrodes"
)

```

## Build, Visualize, & Run

Please make sure the following code block is at the end of your pipeline file. This block will build the pipeline and generate a `make-TEMPLATE.R` script with your pipeline markdown file. `RAVE` will use the generated pipeline script to execute the pipeline in the dashboard application, or in massive production mode.

```{r build, echo=FALSE, results='hide'}
build_pipeline(make_file = "make-TEMPLATE.R")
```


Once the pipeline script `make-TEMPLATE.R` is built, you can visualize and execute the pipeline without the need of re-knit this document. Notice we use `r` block instead of `rave`. (This is because the code blocks are not part of pipeline targets.)

```{r visualize, echo=FALSE}
# Fixed usage, show pipeline graph
try({
  asNamespace("raveio")$pipeline_dependency_graph(
    pipeline_path = ".", glimpse = TRUE)
}, silent = TRUE)
```



